A Task-aware Dual Similarity Network for Fine-grained Few-shot Learning
This addresses the problem of recognizing sub-categories with few samples for computer vision applications, but it is incremental as it builds on existing similarity-based methods.
The paper tackles fine-grained few-shot learning by proposing a Task-aware Dual Similarity Network (TDSNet) that combines global and local similarity measures, achieving competitive performance on three datasets.
The goal of fine-grained few-shot learning is to recognize sub-categories under the same super-category by learning few labeled samples. Most of the recent approaches adopt a single similarity measure, that is, global or local measure alone. However, for fine-grained images with high intra-class variance and low inter-class variance, exploring global invariant features and discriminative local details is quite essential. In this paper, we propose a Task-aware Dual Similarity Network(TDSNet), which applies global features and local patches to achieve better performance. Specifically, a local feature enhancement module is adopted to activate the features with strong discriminability. Besides, task-aware attention exploits the important patches among the entire task. Finally, both the class prototypes obtained by global features and discriminative local patches are employed for prediction. Extensive experiments on three fine-grained datasets demonstrate that the proposed TDSNet achieves competitive performance by comparing with other state-of-the-art algorithms.